Predicting the Impact of Central Bank Communications on Financial Market Investors' Interest Rate Expectations

In this paper, we design an automated system that predicts the impact of central bank communications on investors' interest rate expectations. Our corpus is the Bank of England's 'Monetary Policy Committee Minutes'. Prior studies suggest that effective communications can mitigate a financial crisis; ineffective communications may exacerbate one. The system described here works in four phases. First, the system employs background knowledge from Wikipedia to identify salient aspects for central bank policy associated with economic growth, prices, interest rates and bank lending. These economic aspects are detected using the TextRank link analysis algorithm. A multinomial Naive Bayesian model then classifies sentences from central bank documents to these aspects. The second phase measures sentiment using a count of terms from the General Inquirer dictionary. The third phase employs Latent Dirichlet Allocation (LDA) to infer topic clusters that may act as intensifiers/diminishers of sentiment associated with the economic aspects. Finally, an ensemble tree combines the phases to predict the impact of the communications on financial market interest rates.